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LOCATION:Foyer 2nd Floor
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UID:submissions.pasc-conference.org_PASC22_sess181_pos159@linklings.com
SUMMARY:P36 - Bayesian Parameter Estimation of Galactic Binaries in LISA D
 ata with Gaussian Process Regression
DESCRIPTION:Poster\n\nP36 - Bayesian Parameter Estimation of Galactic Bina
 ries in LISA Data with Gaussian Process Regression\n\nStrub, Ferraioli, St
 ähler, Schmelzbach, Giardini\n\nThe Laser Interferometer Space Antenna (LI
 SA), which is currently under construction, aims to measure gravitational 
 waves in the milli-Hertz frequency band. It is expected that tens of milli
 ons of Galactic binaries will be the dominant sources of gravitational wav
 es. The Galactic binaries at mHz frequencies emit quasi monochromatic grav
 itational waves which will be constantly measured by LISA. To resolve as m
 any Galactic binaries as possible is a central challenge of the upcoming L
 ISA data set. Although it is estimated that tens of thousands of these ove
 rlapping gravitational wave signals are resolvable, and the rest blurs int
 o a galactic foreground noise; extracting tens of thousands of signals usi
 ng Bayesian approaches is still computationally expensive. Hence, in this 
 contribution we describe an end-to-end pipeline with a new approach using 
 Gaussian Process Regression to model the likelihood function in order to r
 apidly compute Bayesian posterior distributions.
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